The use of multi-machine information systems is becoming an increasingly popular component of a wide variety of fields of activity. The increase in the number of problems solved through information systems and the increase in their complexity makes the study of the functional stability of the systems under consideration increasingly relevant. The functional stability of an information system is understood as the ability of this system to perform specified functions under the influence of negative influences. At the moment, a number of indicators have been developed to evaluate functional stability numerically. One such indicator is the convolution of the connectivity matrix. Convolution of the connectivity matrix, despite its completeness, has one very significant drawback: its calculation is a rather complicated procedure. Based on this, the question of approximate calculation of this indicator is logical. Since the convolution of the connectivity matrix can be considered as a function of the probability of serviceability of communication lines, an attempt to use certain methods of approximation theory is obvious. At the moment, methods of approximation theory are quite developed. Some of these methods are quite well researched, while others are just gaining popularity. The first group includes Lagrange interpolation polynomials, Legendre polynomials, Bernstein polynomials, splines, partial sums of series, etc., and the second group includes machine learning models, including feedforward neural networks, regression models, and others. Accordingly, the question immediately arises: which of these methods would allow better approximation of the convolution of the connectivity matrix and under what conditions? This paper provides a comparative analysis of the quality of approximation of the functional connectivity indicator of an information system based on the convolution of the connectivity matrix of this system using often considered feed-forward neural networks and using Bernstein polynomials, which, unfortunately, are not often considered despite their remarkable properties. The specifics of using each of these methods when approaching are also demonstrated and, based on this, it is indicated when which of these methods is better to use.
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